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00:00:19
PROFESSOR: All right, so today
we're returning to simulations.
00:00:25
And I'm going to do at first, a
little bit more abstractly, and
00:00:30
then come back to some details.
00:00:32
So they're different ways to
classify simulation models.
00:00:38
The first is whether it's
stochastic or deterministic.
00:00:53
And the difference here is in a
deterministic simulation, you
00:00:57
should get the same result
every time you run it.
00:01:04
And there's a lot of
uses we'll see for
00:01:06
deterministic simulations.
00:01:08
And then there's stochastic
simulations, where the answer
00:01:14
will differ from run to run
because there's an element
00:01:17
of randomness in it.
00:01:20
So here if you run it again and
again you get the same outcome
00:01:23
every time, here you may not.
00:01:29
So, for example, the problem
set that's due today --
00:01:35
is that a stochastic or
deterministic simulation?
00:01:42
Stochastic, exactly.
00:01:45
And that's what we're going to
focus on in this class, because
00:01:49
one of the interesting
questions we'll see about
00:01:51
stochastic simulations is, how
often do have to run them
00:01:58
before you believe the answer?
00:02:00
And that turns out to be
a very important issue.
00:02:03
You run it once, you get
an answer, you can't
00:02:05
take it to the bank.
00:02:07
Because the next time you run
it, you may get a completely
00:02:09
different answer.
00:02:11
So that will get us a little
bit into the whole issue
00:02:14
of statistical analysis.
00:02:19
Another interesting dichotomy
is static vs dynamic.
00:02:31
We'll look at both, but
will spend more time
00:02:35
on dynamic models.
00:02:37
So the issue --
it's not my phone.
00:02:43
If it's your mother, you
could feel free to take it,
00:02:45
otherwise -- OK, no problem.
00:02:54
In a dynamic situation,
time plays a role.
00:02:57
And you look at how
things evolve over time.
00:03:00
In a static simulation, there
is no issue with time.
00:03:07
We'll be looking at both, but
most of the time we'll be
00:03:11
focusing on dynamic ones.
00:03:15
So an example of this
kind of thing would be a
00:03:19
queuing network model.
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